Correlation measures the strength and direction of the linear relationship between two variables. It quantifies how closely the values of one variable are associated with the values of another variable.

**For example,** let’s consider a dataset that includes the number of hours studied (X) and the corresponding exam scores (Y) of a group of students. By calculating the correlation coefficient, we can determine the strength and direction of the relationship between hours studied and exam scores. A correlation coefficient of +0.8 indicates a strong positive correlation, meaning that as the number of hours studied increases, the exam scores tend to increase as well. Conversely, a correlation coefficient of -0.6 indicates a moderate negative correlation, suggesting that as the number of hours studied increases, the exam scores tend to decrease.

Correlation does not imply causation, meaning that even though two variables may be strongly correlated, it does not necessarily mean that one variable is causing the changes in the other. It simply measures the degree of association between the variables.